MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866915939141812224 |
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| author | Chen, Jianwen Yang, Xinyu Xia, Peng Azarang, Arian Lee, Yueh Z Li, Gang Zhu, Hongtu Li, Yun Chen, Beidi Yao, Huaxiu |
| author_facet | Chen, Jianwen Yang, Xinyu Xia, Peng Azarang, Arian Lee, Yueh Z Li, Gang Zhu, Hongtu Li, Yun Chen, Beidi Yao, Huaxiu |
| contents | Large language models (LLMs) have demonstrated strong performance and rapid progress in a wide range of medical reasoning tasks. However, their sequential autoregressive decoding forces inherently parallel clinical reasoning, such as differential diagnosis, into a single linear reasoning path, limiting both efficiency and reliability for complex medical problems. To address this, we propose MedVerse, a reasoning framework for complex medical inference that reformulates medical reasoning as a parallelizable directed acyclic graph (DAG) process based on Petri net theory. The framework adopts a full-stack design across data, model architecture, and system execution. For data creation, we introduce the MedVerse Curator, an automated pipeline that synthesizes knowledge-grounded medical reasoning paths and transforms them into Petri net-structured representations. At the architectural level, we propose a topology-aware attention mechanism with adaptive position indices that supports parallel reasoning while preserving logical consistency. Systematically, we develop a customized inference engine that supports parallel execution without additional overhead. Empirical evaluations show that MedVerse improves strong general-purpose LLMs by up to 8.9%. Compared to specialized medical LLMs, MedVerse achieves comparable performance while delivering a 1.3x reduction in inference latency and a 1.7x increase in generation throughput, enabled by its parallel decoding capability. Code is available at https://github.com/aiming-lab/MedVerse. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07529 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution Chen, Jianwen Yang, Xinyu Xia, Peng Azarang, Arian Lee, Yueh Z Li, Gang Zhu, Hongtu Li, Yun Chen, Beidi Yao, Huaxiu Machine Learning Large language models (LLMs) have demonstrated strong performance and rapid progress in a wide range of medical reasoning tasks. However, their sequential autoregressive decoding forces inherently parallel clinical reasoning, such as differential diagnosis, into a single linear reasoning path, limiting both efficiency and reliability for complex medical problems. To address this, we propose MedVerse, a reasoning framework for complex medical inference that reformulates medical reasoning as a parallelizable directed acyclic graph (DAG) process based on Petri net theory. The framework adopts a full-stack design across data, model architecture, and system execution. For data creation, we introduce the MedVerse Curator, an automated pipeline that synthesizes knowledge-grounded medical reasoning paths and transforms them into Petri net-structured representations. At the architectural level, we propose a topology-aware attention mechanism with adaptive position indices that supports parallel reasoning while preserving logical consistency. Systematically, we develop a customized inference engine that supports parallel execution without additional overhead. Empirical evaluations show that MedVerse improves strong general-purpose LLMs by up to 8.9%. Compared to specialized medical LLMs, MedVerse achieves comparable performance while delivering a 1.3x reduction in inference latency and a 1.7x increase in generation throughput, enabled by its parallel decoding capability. Code is available at https://github.com/aiming-lab/MedVerse. |
| title | MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.07529 |